Suitability of North Dakota for Conducting Effective Hygroscopic Seeding

Abstract

One goal of the Polarimetric Cloud Analysis and Seeding Test (POLCAST) project is to determine if North Dakota clouds created by surface-base convection are suitable for treatment with hygroscopic flares to enhance surface rainfall amounts. The suitability evaluation examines the processes involved in the hygroscopic seeding conceptual model to determine how supportive North Dakota's environment is to conducting an effective rain enhancement program. POLCAST field measurements are used to determine if important environmental factors support an increase in cloud precipitation efficiency from hygroscopic seeding. Current scientific theories and modeling results indicate that the most important environmental factors are cloud condensation nuclei (CCN) concentrations, droplet size distribution, and cloud base temperature and height. North Dakota's high CCN concentration supports the conclusion that releasing large hygroscopic particles at cloud base produces more collector droplets which increases precipitation efficiency. North Dakota's cloud base temperatures and several kilometer thick clouds indicates that ice phase hydrometeors are important in the precipitation formation process. Hence, increases in precipitation efficiency is not a simple warm rain process but involves more graupel production in the cold cloud region. North Dakota's low cloud base heights indicate that increases in precipitation would increase rain at the surface. All environmental factors examined indicate North Dakota's suitability for conducting hygroscopic seeding to enhance precipitation; however, some cloud processes are impossible to fully evaluate with the current POLCAST data set. A complete aerosol/cloud physics data set would provide a valuable resource for further understanding physical processes and help in constructing a more accurate regional precipitation forecast model. Development, validation, and use of a precipitation forecast model with a known uncertainty would be an effective method for determining precipitation increases from hygroscopic seeding.